Texture image retrieval using adaptive tetrolet transforms

This paper proposes a novel technique for texture image retrieval based on tetrolet transforms. Tetrolets provide fine texture information due to its different way of analysis. Tetrominoes are applied at each decomposition level of an image and best combination of tetrominoes is selected, which better shows the geometry of an image at each level. All three high pass components of the decomposed image at each level are used as input values for feature extraction. A feature vector is created by taking standard deviation in combination with energy at each subband. Retrieval performance in terms of accuracy is tested on group of texture images taken from benchmark databases: Brodatz and VisTex. Experimental results indicate that the proposed method achieves 78.80% retrieval accuracy on group of texture images D1 (taken from Brodatz), 84.41% on group D2 (taken from VisTex) and 77.41% on rotated texture image group D3 (rotated images from Brodatz).     

Robust image retrieval with hidden classes

For the purpose of content-based image retrieval (CBIR), image classification is important to help improve the retrieval accuracy and speed of the retrieval process. However, the CBIR systems that employ image classification suffer from the problem of hidden classes. The queries associated with hidden classes cannot be accurately answered using a traditional CBIR system. To address this problem, a robust CBIR scheme is proposed that incorporates a novel query detection technique and a self-adaptive retrieval strategy. A number of experiments carried out on the two popular image datasets demonstrate the effectiveness of the proposed scheme.     

Web image retrieval using majority-based ranking approach

Web image retrieval has characteristics different from typical content-based image retrieval; web images have associated textual cues. However, a web image retrieval system often yields undesirable results, because it uses limited text information such as surrounding text, URLs, and image filenames. In this paper, we propose a new approach to retrieval, which uses the image content of retrieved results without relying on assistance from the user. Our basic hypothesis is that more popular images have a higher probability of being the ones that the user wishes to retrieve. According to this hypothesis, we propose a retrieval approach that is based on a majority of the images under consideration. We define four methods for finding the visual features of majority of images; (1) majority-first method, (2) centroid-of-all method, (3) centroid-of-top K method, and (4) centroid-of-largest-cluster method. In addition, we implement a graph/picture classifier for improving the effectiveness of web image retrieval. We evaluate the retrieval effectiveness of both our methods and conventional ones by using precision and recall graphs. Experimental results show that the proposed methods are more effective than conventional keyword-based retrieval methods.     

Exploring statistical correlations for image retrieval

Bridging the cognitive gap in image retrieval has been an active research direction in recent years, of which a key challenge is to get enough training data to learn the mapping functions from low-level feature spaces to high-level semantics. In this paper, image regions are classified into two types: key regions representing the main semantic contents and environmental regions representing the contexts. We attempt to leverage the correlations between types of regions to improve the performance of image retrieval. A Context Expansion approach is explored to take advantages of such correlations by expanding the key regions of the queries using highly correlated environmental regions according to an image thesaurus. The thesaurus serves as both a mapping function between image low-level features and concepts and a store of the statistical correlations between different concepts. It is constructed through a data-driven approach which uses Web data (images, their surrounding textual annotations) as training data source to learn the region concepts and to explore the statistical correlations. Experimental results on a database of 10,000 general-purpose images show the effectiveness of our proposed approach in both improving search precision (i.e. filter irrelevant images) and recall (i.e. retrieval relevant images whose context may be varied). Several major factors which have impact on the performance of our approach are also studied.     

Content-based image retrieval using joint correlograms

The comparison of digital images to determine their degree of similarity is one of the fundamental problems of computer vision. Many techniques exist which accomplish this with a certain level of success, most of which involve either the analysis of pixel-level features or the segmentation of images into sub-objects that can be geometrically compared. In this paper we develop and evaluate a new variation of the pixel feature and analysis technique known as the color correlogram in the context of a content-based image retrieval system. Our approach is to extend the autocorrelogram by adding multiple image features in addition to color. We compare the performance of each index scheme with our method for image retrieval on a large database of images. The experiment shows that our proposed method gives a significant improvement over histogram or color correlogram indexing, and it is also memory-efficient.

Content-based image retrieval using growing hierarchical self-organizing quadtree map

In this paper, a growing hierarchical self-organizing quadtree map (GHSOQM) is proposed and used for a content-based image retrieval (CBIR) system. The incorporation of GHSOQM in a CBIR system organizes images in a hierarchical structure. The retrieval time by GHSOQM is less than that by using direct image comparison using a flat structure. Furthermore, the ability of incremental learning enables GHSOQM to be a prospective neural-network-based approach for CBIR systems. We also propose feature matrices, image distance and relevance feedback for region-based images in the GHSOQM-based CBIR system. Experimental results strongly demonstrate the effectiveness of the proposed system.     

A genetic programming framework for content-based image retrieval

The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination do not make sense always and the combined similarity function can be more complex than weight-based functions to better satisfy the users’ expectations. We address this problem by presenting a Genetic Programming framework to the design of combined similarity functions. Our method allows nonlinear combination of image similarities and is validated through several experiments, where the images are retrieved based on the shape of their objects. Experimental results demonstrate that the GP framework is suitable for the design of effective combinations functions.     

Image retrieval based on the texton co-occurrence matrix

This paper put forward a new method of co-occurrence matrix to describe image features. This method can express the spatial correlation of textons. During the course of feature extracting, we have quantized the original images into 256 colors and computed color gradient from the RGB vector space, and then calculated the statistical information of textons to describe image features. Image retrieval experimental results have shown that our proposed method has the discrimination power of color, texture and shape features, the performances are better than that of GLCM and CCG.     

Content-based image retrieval with relevance feedback using random walks

In this paper, we propose a novel approach to content-based image retrieval with relevance feedback, which is based on the random walker algorithm introduced in the context of interactive image segmentation. The idea is to treat the relevant and non-relevant images labeled by the user at every feedback round as “seed” nodes for the random walker problem. The ranking score for each unlabeled image is computed as the probability that a random walker starting from that image will reach a relevant seed before encountering a non-relevant one. Our method is easy to implement, parameter-free and scales well to large datasets. Extensive experiments on different real datasets with several image similarity measures show the superiority of our method over different recent approaches.     

PicSOM – content-based image retrieval with self-organizing maps

We have developed a novel system for content-based image retrieval in large, unannotated databases. The system is called PicSOM, and it is based on tree structured self-organizing maps (TS-SOMs). Given a set of reference images, PicSOM is able to retrieve another set of images which are similar to the given ones. Each TS-SOM is formed with a different image feature representation like color, texture, or shape. A new technique introduced in PicSOM facilitates automatic combination of responses from multiple TS-SOMs and their hierarchical levels. This mechanism adapts to the user's preferences in selecting which images resemble each other. Thus, the mechanism implements a relevance feedback technique on content-based image retrieval. The image queries are performed through the World Wide Web and the queries are iteratively refined as the system exposes more images to the user.     

A new matching strategy for content based image retrieval system

Adopting effective model to access the desired images is essential nowadays with the presence of a huge amount of digital images. The present paper introduces an accurate and rapid model for content based image retrieval process depending on a new matching strategy. The proposed model is composed of four major phases namely: features extraction, dimensionality reduction, ANN classifier and matching strategy. As for the feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). However, integrating multiple features can overcome the problems of single feature, but the system works slowly mainly because of the high dimensionality of the feature space. Therefore, the dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. Consequently, these features reduce the calculation work and the computation time in the retrieval process. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. In addition, the proposed model presents an effective feature matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class. Finally, the results presented in this paper demonstrate that the proposed model provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models.     

Content-based image retrieval by using tree-structured features and multi-layer self-organizing map

A new approach for content-based image retrieval (CBIR) is described. In this study, a tree-structured image representation together with a multi-layer self-organizing map (MLSOM) is proposed for efficient image retrieval. In the proposed tree-structured image representation, a root node contains the global features, while child nodes contain the local region-based features. This approach hierarchically integrates more information of image contents to achieve better retrieval accuracy compared with global and region features individually. MLSOM in the proposed method provides effective compression and organization of tree-structured image data. This enables the retrieval system to operate at a much faster rate than that of directly comparing query images with all images in databases. The proposed method also adopts a relevance feedback scheme to improve the retrieval accuracy by a respectable level. Our obtained results indicate that the proposed image retrieval system is robust against different types of image alterations. Comparative results corroborate that the proposed CBIR system is promising in terms of accuracy, speed and robustness.     

Web-based image indexing and retrieval in JPEG compressed domain

As the majority of content-based image retrieval systems operate on full images in pixel domain, decompression is a prerequisite for the retrieval of compressed images. To provide a possible on-line indexing and retrieval technique for those jpg image files, we propose a novel pseudo-pixel extraction algorithm to bridge the gap between the existing image indexing technology, developed in the pixel domain, and the fact that an increasing number of images stored on the Web are already compressed by JPEG at the source. Further, we describe our Web-based image retrieval system, WEBimager, by using the proposed algorithm to provide a prototype visual information system toward automatic management, indexing, and retrieval of compressed images available on the Internet. This provides users with efficient tools to search the Web for compressed images and establish a database or a collection of special images to their interests. Experiments using texture- and colour-based indexing techniques support the idea that the proposed algorithm achieves significantly better results in terms of computing cost than their full decompression or partial decompression counterparts. This technology will help control the explosion of media-rich content by offering users a powerful automated image indexing and retrieval tool for compressed images on the Web.J. Jiang: Contacting author     

Content-based image retrieval using fuzzy perceptual feedback

In this paper, a new framework called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems is introduced. Conventional binary labeling scheme in relevance feedback requires a crisp decision to be made on the relevance of the retrieved images. However, it is inflexible as user interpretation of visual content varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information requests. It is, therefore, inadequate to describe the user’s fuzzy perception of image similarity with crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgement. It integrates the user’s fuzzy interpretation of visual content into the notion of relevance feedback. An efficient learning approach is proposed using a fuzzy radial basis function (FRBF) network. The network is constructed based on the user’s feedbacks. The underlying network parameters are optimized by adopting a gradient-descent training strategy due to its computational efficiency. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed method.

基于颜色和梯度方向共生直方图的图像检索

为有效融合图像的形状、颜色等特征,提出一种基元梯度方向共生直方图和颜色直方图的彩色图像检索算法。首先将图像划分为4×4互不重叠的基元,并根据基元的梯度平均幅值把基元分为视觉均衡基元和视觉非均衡基元;接着计算视觉均衡基元的平均颜色值,并将其在HSV空间中量化到72色颜色空间,构建视觉均衡基元颜色直方图作为彩色图像的局部颜色描述子;然后将图像中各像素点颜色值在HSV空间量化到72色颜色空间,获得图像的全局颜色直方图;最后在视觉非均衡基元中构建梯度方向共生直方图描述图像的形状特征。融合局部颜色描述子、全局颜色直方图和形状特征构成彩色图像检索特征矢量。实验结果表明,所提算法能够准确描述彩色图像的颜色和形状特征,具有很好的旋转不变性和尺度不变性。相似性度量非常有效,查全率和查准率均有较大提高。